ChemTS: an efficient python library for de novo molecular generation

Automatic design of organic materials requires black-box optimization in a vast chemical space. In conventional molecular design algorithms, a molecule is built as a combination of predetermined fragments. Recently, deep neural network models such as variational autoencoders and recurrent neural net...

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Vydané v:	Science and technology of advanced materials Ročník 18; číslo 1; s. 972 - 976
Hlavní autori:	Yang, Xiufeng, Zhang, Jinzhe, Yoshizoe, Kazuki, Terayama, Kei, Tsuda, Koji
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States Taylor & Francis 31.12.2017 Taylor & Francis Ltd Taylor & Francis Group
Predmet:	404 Materials informatics / Genomics 60 New topics/Others Algorithms Artificial neural networks Computer simulation Design optimization Fragments Libraries Molecular design Monte Carlo tree search Neural networks New topics/Others Organic materials Python python library recurrent neural network Recurrent neural networks 60 New topics/Others Molecular design Monte Carlo tree search recurrent neural network python library 404 Materials informatics / Genomics
ISSN:	1468-6996, 1878-5514
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